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Abstract
Six-dimensional tracking and control task within an Integrated Cognitive Architecture, as a makeup for automated Six-dimensional tracking and control task default. is a common yet highly complex space operation, challenging the human workload. For space exploration system safety, workload is a critical factor in task design and implementation. This research integrates two cognitive architectures: Queuing Network (QN) & Adaptive Control of Thought-Rational (ACT-R) to develop a rigorous computational model for Six-dimensional tracking and control task cognition process. ACT-R represents the human mind as a production rule system. Experiments are set up to build Six-dimensional tracking and control task cognition model and afterwards to validate feasibility of the proposed integrated cognition architecture. Ten subjects of similar training level are chosen to finish manual Six-dimensional tracking and control task with three task difficulty level: one only with displacement margin, one only with posture margin and one with displacement and posture margin. Cognition task analysis is firstly conducted on task performance of subjects. Cognition model of manual Six-dimensional tracking and control task is then built up based on the proposed integration architecture. The proposed integration model developed in the ACTR-QN describes component processes of tracking, decision making and controlling in a 3D environment by ACT-R production rules within QN network. Workload index for each cognition module is calculated based on sector utility throughout the whole task. Human results are compared with the modeled results in the dimension of task time and displacement/posture control trajectory deviation. Workload index is calculated based on the percentage of each module in the time dimension.
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A theory of the way working memory capacity constrains comprehension is proposed. The theory proposes that both processing and storage are mediated by activation and that the total amount of activation available in working memory varies among individuals. Individual differences in working memory capacity for language can account for qualitative and quantitative differences among college-age adults in several aspects of language comprehension. One aspect is syntactic modularity: The larger capacity of some individuals permits interaction among syntactic and pragmatic information, so that their syntactic processes are not informationally encapsulated. Another aspect is syntactic ambiguity: The larger capacity of some individuals permits them to maintain multiple interpretations. The theory is instantiated as a production system model in which the amount of activation available to the model affects how it adapts to the transient computational and storage demands that occur in comprehension.
The Queueing Network - Model Human Processor (QN-MHP) is a computational architecture that combines the mathematical theories and simulation methods of queueing networks (QN) with the symbolic and procedure methods of a GOMS-style task description and the Model Human Processor (MHP). Using QN-MHP, a steering model was created to represent the concurrent perceptual, cognitive, and motor activities involved in vehicle steering as truly concurrent processes. The model was compared with driving performance of human subjects and demonstrated realistic steering behavior. It steered a simulated vehicle at a fixed speed within the lane boundaries of straight sections and curves of different radii. In a quantitative validation of several basic measures of driving performance, the steering model yielded steering angle and lateral position similar to the human subject data. This work showed the strength of QN- MHP as a model of driving behavior. Ongoing work further develops the model by expanding the scope of the driving task and by adding a concurrent secondary in-vehicle task.
Queueing Network-Model Human Processor (QN-MHP) is a computational architecture that integrates two complementary approaches to cognitive modeling: the queueing network approach and the symbolic approach (exemplified by the MHP/GOMS family of models, ACT-R, EPIC, and SOAR). Queueing networks are particularly suited for modeling parallel activities and complex structures. Symbolic models have particular strength in generating a person's actions in specific task situations. By integrating the two approaches, QN-MHP offers an architecture for mathematical modeling and real-time generation of concurrent activities in a truly concurrent manner. QN-MHP expands the three discrete serial stages of MHP, of perceptual, cognitive, and motor processing, into three continuous-transmission subnetworks of servers, each performing distinct psychological functions specified with a GOMS-style language. Multitask performance emerges as the behavior of multiple streams of information flowing through a network, with no need to devise complex, task-specific procedures to either interleave production rules into a serial program (ACT-R), or for an executive process to interactively control task processes (EPIC). Using QN-MHP, a driver performance model was created and interfaced with a driving simulator to perform a vehicle steering, and a map reading task concurrently and in real time. The performance data of the model are similar to human subjects performing the same tasks.
An integrated driver model is presented in which cognitive constructs such as driver needs are causally connected to the orchestration of skill based driving tasks. The proposed three layer hierarchical structure is composed of satisficing décision makers who communicate with intermediate layers of dynamic mental models. The décision makers direct the information flow and decide which mental model is consulted and/or activated and when. Mental models on the other hand provide information at different levéis of abstraction that guides the décision making process. With the aid of tbis hierarchical driver model, prédictions can be made about how automation of particular driving subtask may influence the overall driving behavior and to what degree these may be driver dépendent. We particularly focus on the effects of introducing an Adaptive Cruise Control (ACC) System by exploring the dynamics of the driver's ACC mental model as a function of expérience with the ACC.
This article examines the use of reaction time (RT) to infer the possible configurations of mental systems and presents a class of queueing network models of elementary mental processes. The models consider the temporal issue of discrete versus continuous information transmission in conjunction with the architectural issue of serial versus network arrangement of mental processes. Five elementary but important types of queueing networks are described in detail with regard to their predictions for RT behavior, and they are used to re-examine existing models for psychological processes. As continuous-transmission networks in the general form, queueing network models include the existing discrete and continuous serial models and discrete network models as special cases, cover a broader range of temporal and architectural structures that mental processes might assume, and can be subjected to empirical tests.
Experienced drivers performed simple steering maneuvers in the absence of continuous visual input. Experiments conducted in a driving simulator assessed drivers' performance of lane corrections during brief visual occlusion and examined the visual cues that guide steering. The dependence of steering behavior on heading, speed, and lateral position at the start of the maneuver was measured. Drivers adjusted steering amplitude with heading and performed the maneuver more rapidly at higher speeds. These dependencies were unaffected by a 1.5-s visual occlusion at the start of the maneuver. Longer occlusions resulted in severe performance degradation. Two steering control models were developed to account for these findings. In the 1st, steering actions were coupled to perceptual variables such as lateral position and heading. In the 2nd, drivers pursued a virtual target in the scene. Both models yielded behavior that closely matches that of human drivers.
The contribution of retinal flow (RF), extraretinal (ER), and egocentric visual direction (VD) information in locomotor control was explored. First, the recovery of heading from RF was examined when ER information was manipulated; results confirmed that ER signals affect heading judgments. Then the task was translated to steering curved paths, and the availability and veracity of VD were manipulated with either degraded or systematically biased RF. Large steering errors resulted from selective manipulation of RF and VD, providing strong evidence for the combination of RF, ER, and VD. The relative weighting applied to RF and VD was estimated. A point-attractor model is proposed that combines redundant sources of information for robust locomotor control with flexible trajectory planning through active gaze.
The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a go (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.
Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.
This paper introduces a robust, real-time system for detecting driver lane changes. Under the framework of a "mind-tracking architecture," the system simulates a set of possible driver intentions and their resulting behaviors using an approximation of a rigorous and validated model of driver behavior. The system compares these simulations with a driver's actual observed behavior, thus inferring the driver's unobservable intentions. The paper demonstrates how this system can detect a driver's intention to change lanes, achieving an accuracy of 85% with a false alarm rate of 4%; detecting 80% of lane changes within 1/2 second and 90% within 1 second; and detecting 90% before the vehicle moves 1/4 of the lane width laterally — that is, approximately when the vehicle first touches the destination lane line.
In evaluating the performance of the driver-vehicle system and in establishing design criteria for favorable vehicle dynamics, a quantitative description of driver steering behavior such as a mathematical model is likely to be helpful. The steering task can be divided into two levels: (1) the guidance level involving the perception of the instantaneous and future course of the forcing function provided by the forward view of the road, and the response to it in an anticipatory open-loop control mode; (2) the stabilization level whereby any occuring deviations from the forcing function are compensated for in a closed-loop control mode. This concept of the duality of the driver's steering activity led to a newly developed two-level model of driver steering behavior. Its parameters were identified on the basis of data measured in driving simulator experiments. The parameter estimates of both levels of the model show significant dependence on the experimental situation which can be characterized by variables such as vehicle speed and desired path curvature.
Deze studie beschrijft een cognitief model van het gedrag van een automobilist bij het naderen en afhandelen van kruisingen. Het model bestuurt een gesimuleerde auto in een gesimuleerde verkeerswereld waarin ook andere auto's en fietsers rondrijden. ... Zie: Samenvatting
This article describes a three-node queueing network model of
human multitask performance to account for interferences between
concurrent spatial and verbal tasks. The model integrates considerations
of single-channel queuing theoretic models of selective attention and
parallel processing, multiple-resource models of divided attention, and
provides a computational framework for modeling both the serial
processing and the concurrent execution aspects of human multitask
performance. The single-channel and the multiple-resource concepts and
their applications in engineering models are reviewed. Experimental
evidence in support of the queueing network model is summarized. The
potential value of using queueing network methods to integrate currently
isolated concepts of human multitask performance and in modeling human
machine interaction in general is discussed
Description of the integrated driver model (Tech. Rep. No. FHWA-RD-94-092)
- W H Levison
- N L Cramer